2c1e3771e9
- libs/ai-services: new POST /neighborhood/score router computing weighted 6-axis livability score from per-category POI counts; algorithm versioned for future iteration (sigmoid curves, percentile thresholds). - apps/api: HttpNeighborhoodScoreService proxies to Python first, falls back to PrismaNeighborhoodScoreService when AI service unavailable. Mirrors the HttpAVMService pattern. Existing GET /analytics/neighborhoods/:district/score endpoint and CQRS handler now flow through the proxy. - AnalyticsModule binds Http variant by default, retains Prisma variant as injectable fallback. - Tests: 5 pytest cases for Python heuristic, 4 vitest cases for HTTP proxy fallback behaviour. Co-Authored-By: Paperclip <noreply@paperclip.ing>
34 lines
1.4 KiB
Python
34 lines
1.4 KiB
Python
from pydantic import BaseModel, Field
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class NeighborhoodPOICounts(BaseModel):
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education: int = Field(0, ge=0, description="SCHOOL + UNIVERSITY within 2km")
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healthcare: int = Field(0, ge=0, description="HOSPITAL + CLINIC within 3km")
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transport: int = Field(0, ge=0, description="METRO_STATION + BUS_STOP within 1km")
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shopping: int = Field(0, ge=0, description="MALL + MARKET + SUPERMARKET within 2km")
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greenery: int = Field(0, ge=0, description="PARK within 1km")
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safety: int = Field(0, ge=0, description="POLICE_STATION + FIRE_STATION within 3km")
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class NeighborhoodScoreRequest(BaseModel):
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district: str = Field(..., min_length=1, description="District name (e.g. Quận 1)")
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city: str = Field(..., min_length=1, description="City name (e.g. Hồ Chí Minh)")
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poi_counts: NeighborhoodPOICounts = Field(
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...,
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description="Per-category POI counts already filtered by radius in NestJS",
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)
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class NeighborhoodScoreResponse(BaseModel):
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district: str
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city: str
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education_score: float = Field(..., ge=0, le=10)
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healthcare_score: float = Field(..., ge=0, le=10)
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transport_score: float = Field(..., ge=0, le=10)
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shopping_score: float = Field(..., ge=0, le=10)
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greenery_score: float = Field(..., ge=0, le=10)
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safety_score: float = Field(..., ge=0, le=10)
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total_score: float = Field(..., ge=0, le=100)
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poi_counts: dict[str, int]
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algorithm_version: str
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